System and method for determining target feature focus in image-based overlay metrology

ABSTRACT

A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

TECHNICAL FIELD

The present disclosure relates generally to overlay metrology and, moreparticularly, to machine learning for target feature focus.

BACKGROUND

Image-based overlay metrology may typically include determining relativeoffsets between two or more layers on a sample based on relative imagedpositions of features of an overlay target in the different layers ofinterest. The accuracy of the overlay measurement may thus be sensitiveto image quality associated with imaged features on each sample layer,which may vary based on factors such as a depth of field or location ofthe plane (e.g., focal position) with respect to the sample.Accordingly, overlay metrology procedures typically include tradeoffsbetween image quality at particular sample layers and throughput. Forexample, it may be the case that overlay measurements based on separateimages of each sample layer may provide the highest quality images ofoverlay target features. However, capturing multiple images per targetmay reduce throughput. By way of another example, overlay measurementsbased on a single image capturing features on multiple layers mayprovide relatively higher throughput, but may require referencemeasurements based on external tools or full-wafer measurements toprovide a desired measurement accuracy. Therefore, it would be desirableto provide a system and method for curing defects such as thoseidentified above.

SUMMARY

A metrology system is disclosed, in accordance with one or moreembodiments of the present disclosure. In one embodiment, the metrologysystem includes a controller communicatively coupled to one or morethrough-focus imaging metrology sub-systems, wherein the controllerincludes one or more processors configured to execute a set of programinstructions stored in memory, and wherein the set of programinstructions is configured to cause the one or more processors to:receive a plurality of training images captured at one or more focalpositions, the plurality of training images including one or moretraining features of a training specimen; generate a machine learningclassifier based on the plurality of training images captured at one ormore focal positions; receive one or more target feature selections forone or more target overlay measurements corresponding to one or moretarget features of a target specimen; determine one or more target focalpositions based on the one or more target feature selections using themachine learning classifier; receive one or more target images capturedat the one or more target focal positions, the one or more target imagesincluding the one or more target features of the target specimen; anddetermine one or more overlay measurements based on the one or moretarget images.

A metrology system is disclosed, in accordance with one or moreembodiments of the present disclosure. In one embodiment, the metrologysystem includes one or more through-focus imaging metrology sub-systems.In another embodiment, the metrology system includes a controllercommunicatively coupled to the one or more metrology sub-systems,wherein the controller includes one or more processors configured toexecute a set of program instructions stored in memory, and wherein theset of program instructions is configured to cause the one or moreprocessors to: receive a plurality of training images captured at one ormore focal positions, the plurality of training images including one ormore training features of a training specimen; generate a machinelearning classifier based on the plurality of training images capturedat one or more focal positions; receive one or more target featureselections for one or more target overlay measurements corresponding toone or more target features of a target specimen; determine one or moretarget focal positions based on the one or more target featureselections using the machine learning classifier; receive one or moretarget images captured at the one or more target focal positions, theone or more target images including the one or more target features ofthe target specimen; and determine one or more overlay measurementsbased on the one or more target images.

A method for measuring overlay using one or more through-focus imagingmetrology sub-systems is disclosed, in accordance with one or moreembodiments of the present disclosure. In one embodiment, the methodincludes receiving a plurality of training images captured at one ormore focal positions, the plurality of training images including one ormore training features of a training specimen. In another embodiment,the method includes generating a machine learning classifier based onthe plurality of training images captured at one or more focalpositions. In another embodiment, the method includes receiving one ormore target feature selections for one or more target overlaymeasurements corresponding to one or more target features of a targetspecimen. In another embodiment, the method includes determining one ormore target focal positions based on the one or more target featureselections using the machine learning classifier. In another embodiment,the method includes receiving one or more target images captured at theone or more target focal positions, the one or more target imagesincluding the one or more target features of the target specimen. Inanother embodiment, the method includes determining one or more overlaymeasurements based on the one or more target images.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not necessarily restrictive of the invention as claimed. Theaccompanying drawings, which are incorporated in and constitute a partof the specification, illustrate embodiments of the invention andtogether with the general description, serve to explain the principlesof the invention.

BRIEF DESCRIPTION OF DRAWINGS

The numerous advantages of the disclosure may be better understood bythose skilled in the art by reference to the accompanying figures inwhich:

FIG. 1 is a conceptual view illustrating a metrology system, inaccordance with one or more embodiments of the present disclosure.

FIG. 2 is a simplified schematic view illustrating a metrology system,in accordance with one or more embodiments of the present disclosure.

FIG. 3 is a flow diagram illustrating steps performed in a method formeasuring overlay, in accordance with one or more embodiments of thepresent disclosure.

FIG. 4 is a flow diagram illustrating steps performed in a method formeasuring overlay, in accordance with one or more embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the subject matter disclosed,which is illustrated in the accompanying drawings. The presentdisclosure has been particularly shown and described with respect tocertain embodiments and specific features thereof. The embodiments setforth herein are taken to be illustrative rather than limiting. Itshould be readily apparent to those of ordinary skill in the art thatvarious changes and modifications in form and detail may be made withoutdeparting from the spirit and scope of the disclosure. Reference willnow be made in detail to the subject matter disclosed, which isillustrated in the accompanying drawings.

Embodiments of the present disclosure are directed to systems andmethods through-focus imaging of an overlay target on a sample toprovide self-referenced overlay measurement recipes for additionaloverlay targets on the sample as well as process-monitoring betweenwafers.

Semiconductor devices are typically formed as multiple patterned layersof patterned material on a substrate. Each patterned layer may befabricated through a series of process steps such as, but not limitedto, one or more material deposition steps, one or more lithographysteps, or one or more etching steps. Further, features within eachpatterned layer must typically be fabricated within selected tolerancesto properly construct the final device. For example, overlay errorsassociated with relative misregistrations of features on differentsample layers must be well characterized and controlled within eachlayer and relative to previously fabricated layers.

Accordingly, overlay targets may be fabricated on one or more samplelayers to enable efficient characterization of the overlay of featuresbetween the layers. For example, an overlay target may includefabricated features on multiple layers arranged to facilitate accurateoverlay measurements. In this regard, overlay measurements on one ormore overlay targets distributed across a sample may be used todetermine the overlay of corresponding device features associated with asemiconductor device being fabricated.

Image-based overlay metrology tools typically capture one or more imagesof an overlay target and determine an overlay between sample layersbased on relative positions of imaged features of the overlay target onlayers of interest. For example, features of overlay targets suitablefor image-based overlay (e.g., box-in-box targets, advanced imagingmetrology (AIM) targets, or the like) located on different sample layersmay be, but are not required to be, arranged such that features on alllayers of interest are simultaneously visible. In this regard, theoverlay may be determined based on relative positions of features onlayers of interest within one or more images of the overlay target.Further, overlay targets may be designed to facilitate overlaymeasurements between any number of sample layers in either a singlemeasurement step or multiple measurement steps. For instance, featureswithin any number of sample layers may be simultaneously visible for asingle-measurement overlay determination between all sample layers. Inanother instance, an overlay target may have different sections (e.g.,cells, or the like) to facilitate overlay measurements between selectedlayers. In this regard, overlay between all layers of interest may bedetermined based on measurements of multiple portions of the overlaytarget.

The accuracy of image-based overlay may depend on multiple factorsassociated with image quality such as, but not limited to, resolution oraberrations. For example, the system resolution may impact the accuracyat which positions of features may be determined (e.g., edge positions,centers of symmetry, or the like). By way of another example,aberrations in an imaging system may distort the sizes, shapes, andspacings of features such that position measurements based on an imagemay not accurately represent the physical sample. Further, image qualitymay vary as a function of focal position. For example, features outsideof a focal volume of an imaging system may appear blurred and/or mayhave a lower contrast between overlay target features and backgroundspace than features within the focal volume, which may impact theaccuracy of positional measurements (e.g., edge measurements, or thelike).

Accordingly, it may be the case that capturing separate images offeatures on different layers of a sample (e.g., located at differentdepths in the sample) may provide accurate overlay metrologymeasurements. For example, a focal position (e.g., an object plane) ofan image-based overlay metrology system may be adjusted to correspond tothe depth of imaged features on each layer of interest. In this regard,features on each layer of interest may be imaged under conditionsdesigned to mitigate focal position-dependent effects.

However, it is recognized herein that capturing multiple images of anoverlay target at varying depths may negatively impact the throughput ofthe system, which may offset gains in accuracy associated with themultiple images. Embodiments of the present disclosure are directed tooverlay measurements at multiple focal positions, where the multiplefocal positions are determined in real-time by the metrology system andmay correspond to the one or more varying depths at which one or moreportions of one or more metrology targets is located. For example, anoverlay measurement may be generated for an overlay target based onmultiple images captured at multiple focal positions (e.g., includingfocal depths corresponding to locations of overlay target features),where the metrology system determines the multiple focal positions inreal-time (e.g., such as through the use of a machine learningclassifier).

Additional embodiments of the present disclosure are directed totranslating one or more portions of the metrology system along one ormore adjustment axes. For example, it may be the case that an optimalcoordinate position at which a particular overlay measurement is takenmay be different from the optimal coordinate position of a subsequentoverlay measurement.

Additional embodiments of the present disclosure are directed togenerating control signals based on the overlay measurements across thesample provided to process tools (e.g., lithography tools, metrologytools, or the like) as feedback and/or feedforward data.

FIG. 1 is a conceptual view illustrating an overlay metrology system100, in accordance with one or more embodiments of the presentdisclosure. The system 100 may include, but is not limited to, one ormore metrology sub-systems 102. The system 100 may additionally include,but is not limited to, a controller 104, wherein the controller includesone or more processors 106, a memory 108, and a user interface 110.

The one or more metrology sub-systems 102 may include any metrologysub-system known in the art including, but not limited to, an opticalmetrology sub-system. For example, the metrology sub-system 102 mayinclude, but is not limited to, an optical-based metrology system abroadband metrology system (e.g., broadband plasma metrology system) ora narrowband inspection system (e.g., laser-based metrology system). Inanother instance, the metrology sub-system 102 may include ascatterometry-based metrology system. By way of another example, the oneor more metrology sub-systems 102 may include any through-focus imagingmetrology sub-system (e.g., an imaging metrology sub-system configuredto construct one or more images of a specimen, where the one or moreimages are of a desired focus and are constructed using a plurality ofimages of the sample captured at different focal positions).

In one embodiment, the controller 104 is communicatively coupled to theone or more metrology sub-systems 102. In this regard, the one or moreprocessors 106 of the controller 104 may be configured to generate andprovide one or more control signals configured to make one or moreadjustments to one or more portions of the one or more metrologysub-systems 102.

In another embodiment, the controller 104 is configured to receive aplurality of training images captured at one or more focal positions,wherein the plurality of training images includes one or more trainingfeatures of a training specimen. For example, the controller 104 may beconfigured to receive the plurality of training images from the one ormore metrology sub-systems 102.

In another embodiment, the controller 104 may be configured to generatea machine learning classifier based on the plurality of training images.For example, the controller 104 may be configured to use as inputs tothe machine learning classifier the plurality of training images.

In another embodiment, the controller 104 may be configured to receiveone or more target feature selections for one or more target overlaymeasurements, wherein the one or more target feature selectionscorrespond to one or more target features of the target specimen. Forexample, the controller 104 may be configured to receive one or moretarget feature selections from a user via the user interface 110.

In another embodiment, the controller 104 may be configured to determineone or more target focal positions based on the one or more targetfeature selections. For example, the controller 104 may be configured todetermine one or more target focal positions using the machine learningclassifier.

In another embodiment, the controller 104 may be configured to receiveone or more target images captured at the one or more target focalpositions. For example, the controller 104 may be configured to receiveone or more target images including the one or more target features atthe one or more target focal positions.

In another embodiment, the controller 104 may be configured to determineone or more overlay measurements based on the one or more target images.For example, the controller 104 may be configured to determine overlaybetween a first layer of the target specimen and a second layer of thetarget specimen based on one or more target features formed on each ofthe first layer and the second layer.

FIG. 2 illustrates a simplified schematic view of the system 100, inaccordance with one or more embodiments of the present disclosure. Inparticular, the system 100 as depicted in FIG. 2 includes an opticalmetrology sub-system 102 such that system 100 operates as an opticalinspection system.

The optical inspection sub-system 102 may include any optical-basedinspection known in the art. The metrology sub-system 102 may include,but is not limited to, an illumination source 112, an illumination arm111, a collection arm 113, and a detector assembly 126.

In one embodiment, metrology sub-system 102 is configured to inspectand/or measure the specimen 120 disposed on the stage assembly 122. Theillumination source 112 may include any illumination source known in theart for generating illumination 101 including, but not limited to, anillumination source configured to provide wavelengths of lightincluding, but not limited to, vacuum ultraviolet radiation (VUV), deepultraviolet radiation (DUV), ultraviolet (UV) radiation, visibleradiation, or infrared (IR) radiation. In another embodiment, themetrology sub-system 102 may include an illumination arm 111 configuredto direct illumination 101 to the specimen 120. It is noted thatillumination source 112 of the metrology sub-system 102 may beconfigured in any orientation known in the art including, but notlimited to, a dark-field orientation, a light-field orientation, and thelike. For example, one or more optical elements 114, 124 may beselectably adjusted in order to configure the metrology sub-system 102in a dark-field orientation, a bright-field orientation, and the like.

The specimen 120 may include any specimen known in the art including,but not limited to, a wafer, a reticle, a photomask, and the like. Thespecimen 120 may include any specimen having one or more overlaymetrology targets known in the art to be suitable for image-basedoverlay metrology. For example, the specimen 120 may include an overlaymetrology target that includes target features in one or more layerswhich may have been printed in one or more lithographically distinctexposures. The targets and/or the target features may possess varioussymmetries such as two-fold or four-fold rotation symmetry, reflectionsymmetry.

In one embodiment, the specimen 120 is disposed on a stage assembly 122,wherein the stage assembly 122 is configured to facilitate movement ofspecimen 120 (e.g., movement along one or more of an x-direction, ay-direction, or a z-direction). In another embodiment, the stageassembly 122 is an actuatable stage. For example, the stage assembly 122may include, but is not limited to, one or more translational stagessuitable for selectably translating the specimen 120 along one or morelinear directions (e.g., x-direction, y-direction and/or z-direction).By way of another example, the stage assembly 122 may include, but isnot limited to, one or more rotational stages suitable for selectivelyrotating the specimen 120 along a rotational direction. By way ofanother example, the stage assembly 122 may include, but is not limitedto, a rotational stage and a translational stage suitable for selectablytranslating the specimen 120 along a linear direction and/or rotatingthe specimen 120 along a rotational direction. It is noted herein thatthe system 100 may operate in any metrology mode known in the art.

The illumination arm 111 may include any number and type of opticalcomponents known in the art. In one embodiment, the illumination arm 111includes one or more optical elements 114, a set of one or more opticalelements 115, a beam splitter 116, and an objective lens 118. In thisregard, the illumination arm 111 may be configured to focus illumination101 from the illumination source 112 onto the surface of the specimen120. The one or more optical elements 114 may include any opticalelements known in the art including, but not limited to, one or moremirrors, one or more lenses, one or more polarizers, one or more beamsplitters, wave plates, and the like.

In another embodiment, the metrology sub-system 102 includes acollection arm 113 configured to collect illumination reflected orscattered from specimen 120. In another embodiment, the collection arm113 may direct and/or focus the reflected and scattered light to one ormore sensors of a detector assembly 126 via one or more optical elements124. The one or more optical elements 124 may include any opticalelements known in the art including, but not limited to, one or moremirrors, one or more lenses, one or more polarizers, one or more beamsplitters, wave plates, and the like. It is noted that detector assembly126 may include any sensor and detector assembly known in the art fordetecting illumination reflected or scattered from the specimen 120.

In another embodiment, the detector assembly 126 of metrology sub-system102 is configured to collect inspection data of the specimen 120 basedon illumination reflected or scattered from the specimen 120. In anotherembodiment, the detector assembly 126 is configured to transmitcollected/acquired images and/or metrology data to the controller 104.

The metrology system 100 may be configured to image the specimen 120 atany selected measurement plane (e.g., at any position along az-direction). For example, a location of an object plane associated withan image generated on the detector assembly 126 with respect to thespecimen 120 may be adjusted using any combination of components of themetrology system 100. For example, the location of the object planeassociated with an image generated on the detector assembly 126 withrespect to the specimen 120 may be adjusted by controlling a position ofthe stage assembly 122 with respect to the objective lens 118. By way ofanother example, the location of the object plane associated with animage generated on the detector assembly 126 with respect to thespecimen 120 may be adjusted by controlling a position of the objectivelens 118 with respect to the specimen 120. For instance, the objectivelens 118 may be mounted on a translation stage configured to adjust aposition of the objective lens 118 along one or more adjustment axes(e.g., an x-direction, a y-direction, or a z-direction). By way ofanother example, the location of the object plane associated with animage generated on the detector assembly 126 with respect to thespecimen 120 may be adjusted by controlling a position of the detectorassembly 126. For instance, the detector assembly 126 may be mounted ona translation stage configured to adjust a position of the detectorassembly 126 along the one or more adjustment axes. By way of anotherexample, the location of the object plane associated with an imagegenerated on the detector assembly 126 with respect to the specimen 120may be adjusted by controlling a position of the one or more opticalelements 124. For instance, one or more optical elements 124 may bemounted on translation stages configured to adjust positions of the ofthe one or more optical elements 124 along the one or more adjustmentaxes. It is specifically noted herein that the controller 104 may beconfigured to perform any of the foregoing adjustments by providing oneor more control signals to one or more portions of the metrologysub-system 102.

As noted previously herein, the controller 104 of system 100 may includeone or more processors 106 and memory 108. The memory 108 may includeprogram instructions configured to cause the one or more processors 106to carry out various process steps described throughout the presentdisclosure. For example, the program instructions are configured tocause the one or more processors 106 to adjust one or morecharacteristics of the metrology sub-system 102 in order to perform oneor more of the process steps of the present disclosure. Further, thecontroller 104 may be configured to receive data including, but notlimited to, imagery data associated with the specimen 120 from thedetector assembly 126.

The one or more processors 106 of the controller 104 may include anyprocessor or processing element known in the art. For the purposes ofthe present disclosure, the term “processor” or “processing element” maybe broadly defined to encompass any device having one or more processingor logic elements (e.g., one or more micro-processor devices, one ormore application specific integrated circuit (ASIC) devices, one or morefield programmable gate arrays (FPGAs), or one or more digital signalprocessors (DSPs)). In this sense, the one or more processors 106 mayinclude any device configured to execute algorithms and/or instructions(e.g., program instructions stored in memory). In one embodiment, theone or more processors 106 may be embodied as a desktop computer,mainframe computer system, workstation, image computer, parallelprocessor, networked computer, or any other computer system configuredto execute a program configured to operate or operate in conjunctionwith the metrology system 100, as described throughout the presentdisclosure

Moreover, different components of the system 100 may include a processoror logic elements suitable for carrying out at least a portion of thesteps described in the present disclosure. Therefore, the abovedescription should not be interpreted as a limitation on the embodimentsof the present disclosure but merely as an illustration. Further, thesteps described throughout the present disclosure may be carried out bya single controller 104 or, alternatively, multiple controllers.Additionally, the controller 104 may include one or more controllershoused in a common housing or within multiple housings. In this way, anycontroller or combination of controllers may be separately packaged as amodule suitable for integration into metrology system 100. Further, thecontroller 104 may analyze data received from the detector assembly 126and feed the data to additional components within the metrology system100 or external to the metrology system 100.

The memory 108 may include any storage medium known in the art suitablefor storing program instructions executable by the associated one ormore processors 106. For example, the memory 108 may include anon-transitory memory medium. By way of another example, the memory 108may include, but is not limited to, a read-only memory (ROM), arandom-access memory (RAM), a magnetic or optical memory device (e.g.,disk), a magnetic tape, a solid-state drive and the like. It is furthernoted that memory 108 may be housed in a common controller housing withthe one or more processors 106. In one embodiment, the memory 108 may belocated remotely with respect to the physical location of the one ormore processors 106 and controller 104. For instance, the one or moreprocessors 106 of controller 104 may access a remote memory (e.g.,server), accessible through a network (e.g., internet, intranet and thelike).

In one embodiment, the user interface 110 is communicatively coupled tothe controller 104. The user interface 110 may include, but is notlimited to, one or more desktops, laptops, tablets, and the like. Inanother embodiment, the user interface 110 includes a display used todisplay data of the system 100 to a user. The display of the userinterface 110 may include any display known in the art. For example, thedisplay may include, but is not limited to, a liquid crystal display(LCD), an organic light-emitting diode (OLED) based display, or a CRTdisplay. Those skilled in the art should recognize that any displaydevice capable of integration with a user interface 110 is suitable forimplementation in the present disclosure. In another embodiment, a usermay input selections and/or instructions responsive to data displayed tothe user via a user input device of the user interface 110

In another embodiment, the controller 104 is communicatively coupled toone or more elements of the metrology system 100. In this regard, thecontroller 104 may transmit and/or receive data from any component ofthe metrology system 100. Further, the controller 104 may direct orotherwise control any component of the metrology system 100 bygenerating one or more control signals for the associated components.For example, the controller 104 may be communicatively coupled to thedetector assembly 126 to receive one or more images from the detectorassembly 126.

FIG. 3 illustrates a method 300 of measuring overlay, in accordance withone or more embodiments of the present disclosure.

In step 302, a plurality of training images captured at one or morefocal positions is received. For example, a plurality of training images125 may be received by the controller 104 from the metrology sub-system102. In this regard, the plurality of training images 125 may includeoptical training images. In additional and/or alternative embodiments,the controller 104 may be configured to receive one or more trainingimages 125 from a source other than the one or more metrologysub-systems 102. For example, the controller 104 may be configured toreceive one or more training images 125 of features of a specimen 120from an external storage device and/or memory 108. In anotherembodiment, controller 104 may be further configured to store receivedtraining images 125 in memory 108.

The plurality of training images 125 may include one or more trainingfeatures of a training specimen. For example, the plurality of trainingimages 125 may include images captured at multiple depths of thetraining specimen. In this regard, the one or more training features ofthe training specimen may include one or more training target featuresformed at different layers of the training specimen. The metrologysub-system 102 may be configured to capture the plurality of trainingimages 125 at one or more focal positions corresponding to the depth(e.g., position along the z-direction) of a particular training feature.In one embodiment, the metrology sub-system 102 may be configured tocapture the plurality of training images 125 within a focus trainingrange, wherein the focus training range comprises a plurality of focalpositions corresponding to a plurality of depths of the one or moretraining features. The focus training range may be provided by a uservia the user interface 110.

In step 304, a machine learning classifier is generated based on theplurality of training images. For example, the controller 104 may beconfigured to generate a machine learning classifier based on theplurality of training images. The controller 104 may be configured togenerate the machine learning classifier via any one or more techniquesknown in the art, including, without limitation, supervised learning,unsupervised learning, and the like.

For example, in the context of supervised learning, the plurality oftraining images 125 may include varying degrees of focus based on thefocal position at which each of the plurality of training images wascaptured (e.g., the plurality of training images may include a pluralityof through-focus images of one or more features of the sample). In thisregard, the controller 104 may receive one or more optimal focustolerances such that the controller 104 may determine one or moretraining images of the plurality of training images that fall within theone or more optimal focus tolerances. Accordingly, the plurality oftraining images 125 and the one or more optimal focus tolerances may beused as inputs to train the machine learning classifier. The controller104 may be further configured to store the plurality of training images125, the optimal focus tolerances, and the generated machine learningclassifier, in memory 108.

The one or more optimal focus tolerances may be configured such that themachine learning classifier may be configured to determine one or moretarget focal positions for one or more target overlay measurements. Inthis regard, the one or more optimal focus tolerances may be configuredto ensure that one or more target images that may be subsequentlycaptured at the one or more target focal positions are of sufficientquality for overlay measurement by the controller 104. The one or moreoptimal focus tolerances may be provided by a user via the userinterface 110. In another embodiment, the controller 104 may beconfigured to determine the one or more optimal focus tolerances usingany technique known in the art. For example, the controller 104 may beconfigured to determine the one or more optimal focus tolerances basedon a contrast precision function (e.g., a function configured todetermine a focus at which noise is minimal based on a plurality ofimages captured at multiple focal positions). In an alternativeembodiment, the controller 104 may be configured to determine the one ormore optimal focus tolerances using a Linnik interferometer integratedinto or generated by one or more portions of the metrology sub-system102. By way of another example, one or more portions of the metrologysub-system 102 (e.g., the illumination source 112 and/or one or moreportions of the illumination arm 111) may be configured to illuminatethe sample, where the controller 104 may be configured to generate aLinnik interferogram (e.g., a low coherence interferogram) based onillumination collected by the detector assembly 126. In this regard, thecontroller 104 may be configured to determine a peak (e.g., a pointhaving the greatest contrast of all collected images) of theinterferogram and may associate the peak with the through-focus positionalong a z-axis of the sample.

It is specifically noted that the embodiments of the present disclosureare not limited to the controller 104 generating or referencing a Linnikinterferogram in order to determine the one or more optimal focustolerances. For example, the controller 104 may be configured togenerate a machine learning classifier configured to determine bestfocus and/or best position based on a plurality of training imagesgenerated using one or more signals indicative of illumination (e.g.,illumination generated by a bright field and/or dark field microscopydevice) emanating from various focal positions and/or positions (e.g., acoordinate position on an x-axis and/or a y-axis, where the plurality oftraining images may be captured at various coordinate positions alongone or both of such axes, such as via a translatable stage). In at leastthe foregoing embodiment, the controller 104 may be configured todetermine best focus and/or best position based on image contrast and/orcontrast precision of the plurality of training images. In this regard,the machine learning classifier may be configured to determine the oneor more optimal focus tolerances based on the plurality of trainingimages, where the plurality of training images constitute focus sliceimages generated at various focal positions along a z-axis of thesample, and where the controller 104 may be configured to classifyand/or label each focus slice image with a corresponding best focusbased on image contrast and/or contrast precision of the focus sliceimages. In this way, the controller 104 may be configured to determinethe one or more optimal focus tolerances by interpolation based on theplurality of training images.

In another embodiment, the machine learning classifier may be configuredto determine the one or more optimal focus tolerances based on theplurality of training images, where the plurality of training imagesinclude images captured at various focal positions along a z-axis of thesample, and where the focus of the plurality of training images isvaried using a coarse focusing mechanism. It is specifically noted thata coarse focusing mechanism (or any other general focusing mechanismknown in the art to be suitable for the purposes contemplated by thepresent disclosure), may permit the machine learning classifier todetermine the one or more optimal focus tolerances in a more accuratemanner (e.g., more accurate relative to other methods of focusadjustment described herein or known in the art). For example, such asin Step 308 (described herein below), the course focusing mechanism maybe configured to permit the controller 104 to determine and/or set acoarse focus (e.g., a general focal position, which may later be finelyadjusted (see Step 308 below)). It is noted that the coarse focusingsystem may include any coarse focusing mechanism known in the art to besuitable for the purposes contemplated by the present disclosure,including, without limitation, a lens triangulation mechanism, a bi-celldetector apparatus, and/or any range finding system.

It is noted herein that the machine learning classifier generated instep 304 may include any type of machine learning algorithm/classifierand/or deep learning technique or classifier known in the art including,but not limited to, a random forest classifier, a support vector machine(SVM) classifier, an ensemble learning classifier, an artificial neuralnetwork (ANN), and the like. By way of another example, the machinelearning classifier may include a deep convolutional neural network(CNN). For instance, in some embodiments, the machine learningclassifier may include ALEXNET and/or GOOGLENET. In this regard, themachine learning classifier may include any algorithm, classifier, orpredictive model, including, without limitation, any algorithm,classifier, or predictive model configured to generate a Linnikinterferogram and to determine the one or more target focal positionsfor one or more target overlay measurements using the Linnikinterferogram. In some embodiments, the machine learning classifier maycomprise a neural network having multiple layers and receptors. Forexample, the machine learning classifier may comprise a neural networkhaving approximately five layers and approximately fifty receptors.

In Step 306, one or more target feature selections for one or moretarget overlay measurements corresponding to one or more target featuresof a target specimen are received. For example, the controller 104 maybe configured to receive the one or more target feature selections forone or more target overlay measurements from a user via the userinterface 110. The one or more target feature selections may include oneor more signals configured to direct the system 100 to capture one ormore target images including the one or more target features of thetarget specimen. Upon receipt of the one or more target featureselections, the controller 104 may be configured to determine one ormore expected depths of the one or more target features within thetarget specimen. For example, the controller 104 may be provided the oneor more expected depths of the one or more target features for overlaymeasurement by a user via the user interface 110. In another embodiment,the controller 104 may determine the one or more expected depths of theone or more target features for overlay measurement by reference to oneor more design files or other data corresponding to the target specimenstored in memory 108. In another embodiment, the controller 104 maydetermine the one or more expected depths of the one or more targetfeatures for overlay measurement based on the Linnik interferogram. Forexample, the controller 104 may determine the one or more expecteddepths by reference to the peak of the Linnik interferogram associatedwith the through-focus position along the z-axis of the sample.

In Step 308, one or more target focal positions based on the one or moretarget feature selections are determined using the machine learningclassifier. For example, the controller 104 may be configured todetermine the one or more target focal positions based on the one ormore target feature selections using the machine learning classifier. Byway of another example, the controller 104 may provide the one or moreexpected depths of the one or more target features of the targetspecimen as an input to the machine learning classifier. In this regard,the machine learning classifier may be configured to provide the one ormore target focal positions based on the plurality of training images125 and the optimal focus tolerances. It is noted that the machinelearning classifier may be configured to determine one or more targetfocal positions for one or more target overlay measurements bydetermining one or more focal positions within 1 micron of a focalposition provided by the contrast precision function, the Linninkinterferogram function, or any other method described herein.

In some embodiments, upon determination of the one or more target focalpositions based on the one or more target feature selections, thecontroller 104 may be configured to determine and/or provide one or morecontrol signals to one or more portions of the one or more metrologysub-systems 102, wherein the one or more control signals are configuredto cause the one or more portions of the one or more metrologysub-systems 102 to be translated along one or more adjustment axes(e.g., an x-direction, a y-direction, and/or a z-direction). Forexample, the controller 104 may be configured to provide one or morecontrol signals to the stage assembly 122 and/or the detector assembly126 such that the target specimen is located at one of the one or moredetermined target focal positions. In another embodiment, the controller104 may be configured to provide one or more control signals to at leastone of the optical elements 114, 115, the beam-splitter 116, theobjective lens 118, or the optical elements 124, in order to enable themetrology sub-system 102 to capture one or more target images at the oneor more target focal positions.

In the case of a machine learning classifier configured to determine theone or more optimal focus tolerances based on the plurality of trainingimages using a coarse focusing mechanism, the machine learningclassifier may be configured to determine (and the controller 104 may beconfigured to provide) one or more control signals to the coarsefocusing system, where the one or more control signals may be configuredto cause the coarse focusing system adjust focus to a focal positionwithin ±2 micrometers of the target focal position. In this regard, themachine learning classifier may be configured to determine the one ormore optimal focus tolerances by determining one or more fine-focusadjustments to modify the focal position determined by the coursefocusing system.

In Step 310, one or more target images of the one or more targetfeatures captured at the one or more target focal positions arereceived. For example, the controller 104 may be configured to receiveone or more target images 135 from the metrology sub-system 102. As itis used herein, the term “target images” may refer to images of the oneor more target features captured at the one or more target focalpositions and with which one or more overlay measurements will bedetermined. Thus, the term “target images” may be distinguished from“training images” which may be regarded as images of training featureswhich may be used as inputs to train the machine learning classifier.

It is noted herein that any discussion regarding the acquisition oftraining images 125 may be regarded as applying to the acquisition oftarget images 135, unless noted otherwise herein. In additional and/oralternative embodiments, the controller 104 may be configured to receiveone or more target images 135 from a source other than the one or moremetrology sub-systems 102. For example, the controller 104 may beconfigured to receive one or more target images 135 of a specimen 120from an external storage device and/or memory 108.

In Step 312, one or more overlay measurements are determined based onthe one or more target images. For example, the controller 104 may beconfigured to determine an overlay between a first layer of the targetspecimen and a second layer of the target specimen based on a firstoverlay measurement corresponding to one or more target features formedon the first layer of the target specimen and a second overlaymeasurement corresponding to one or more target features formed on thesecond layer of the target specimen. In this regard, the controller 104may be configured to determine an offset (e.g., PPE) between the firstlayer and the second layer. The one or more overlay measurements mayinclude any overlay measurement known in the art to be suitable for thepurposes contemplated by the present disclosure, including those overlaymeasurements configured for use with specific target features of aspecimen. In this regard, the controller 104 may be configured toutilize one or more overlay algorithms stored in memory 108 or otherwiseprovided to the controller 104 in order to determine the one or moreoverlay measurements.

In some embodiments, the method 300 may include a Step 312. In Step 312,one or more control signals are provided. For example, one or morecontrol signals for adjusting one or more process tools (e.g.,lithographic tools) are provided. As an additional example, thecontroller 104 may provide one or more control signals (or correctionsto the control signals) to one or more portions of one or more processtools for adjusting the one or more parameters (e.g., fabricationsettings, configuration, and the like) of the one or more process toolssuch that one or more parameters of the one or more process tools areadjusted. The controller 104 may determine the one or more controlsignals based on the one or more overlay measurements of the specimen.The control signals (or corrections to the control signals) may beprovided by the controller 104 as part of a feedback and/or feedforwardcontrol loop. The controller 104 may cause the one or more process toolsto execute one or more adjustments to the one or more parameters of theprocess tools based on the control signals, or the controller 104 mayalert a user to make the one or more adjustments to the one or moreparameters. In this sense, the one or more control signals maycompensate for errors of one or more fabrication processes of the one ormore process tools, and thus may enable the one or more process tools tomaintain overlay within selected tolerances across multiple exposures onsubsequent samples in the same or different lots.

FIG. 4 illustrates a method 400 for measuring overlay, in accordancewith one or more embodiments of the present disclosure.

In Step 402, one or more target feature selections for one or moretarget overlay measurements corresponding to one or more target featuresof a target specimen are received. For example, the controller 104 maybe configured to receive the one or more target feature selections forone or more target overlay measurements from a user via the userinterface 110. The one or more target feature selections may include oneor more signals configured to direct the system 100 to capture one ormore target images including the one or more target features of thetarget specimen. Upon receipt of the one or more target featureselections, the controller 104 may be configured to determine one ormore expected positions (e.g., positions along an x-direction, ay-direction, and/or a z-direction) of the one or more target featureswithin the target specimen. For example, the controller 104 may beprovided the one or more expected positions of the one or more targetfeatures for overlay measurement by a user via the user interface 110.In another embodiment, the controller 104 may determine the one or moreexpected depths of the one or more target features for overlaymeasurement by reference to one or more design files or other datacorresponding to the target specimen stored in memory 108.

In Step 404, one or more target focal positions based on the one or moretarget feature selections are determined. For example, the controller104 may be configured to determine one or more target focal positionsusing the machine learning classifier based on the one or more targetfeature selections, where the one or more target feature selectionscorrespond to one or more target features for one or more overlaymeasurements. By way of another example, the controller 104 may providethe one or more expected depths of the one or more target features ofthe target specimen as an input to the machine learning classifier. Inthis regard, the machine learning classifier may be configured toprovide the one or more target focal positions based on the plurality oftraining images 125 and the optimal focus tolerances.

In some embodiments, as shown in Step 406, upon determination of the oneor more target focal positions based on the one or more target featureselections, the controller 104 may be configured to determine and/orprovide one or more control signals to one or more portions of the oneor more metrology sub-systems 102, wherein the one or more controlsignals are configured to cause the one or more portions of the one ormore metrology sub-systems 102 to be translated along one or moreadjustment axes (e.g., an x-direction, a y-direction, and/or az-direction). For example, in one embodiment, the controller 104 may beconfigured to provide one or more control signals to the stage assembly122 and/or the detector assembly such that the one or more targetfeatures for overlay measurements are centered within a field of view ofone or more components of the metrology sub-system 102. In this regard,the controller 104 may be configured to provide one or more controlsignals to one or more portions of the metrology sub-system 102 suchthat the target images 135 include the one or more target features atone or more centers of the target images 135. In another embodiment, thecontroller 104 may be configured to provide one or more control signalsto the stage assembly 122 such that the target specimen is located atone of the one or more determined target focal positions. In anotherembodiment, the controller 104 may be configured to provide one or morecontrol signals to at least one of the optical elements 114, 115, thebeam-splitter 116, the objective lens 118, or the optical elements 124,in order to enable the metrology sub-system 102 to capture one or moretarget images at the one or more target focal positions.

It is specifically noted that the controller 104 may be configured toprovide the one or more control signals to the one or more portions ofthe one or more metrology sub-systems 102 simultaneously. For example,the controller 104 may be configured to provide one or more controlsignals configured to cause simultaneous adjustments to the stageassembly 122 along an x-direction and/or a y-direction and to one ormore other portions of the metrology sub-system 102 (e.g., the detectorassembly 126 and/or the objective lens 118) along a z-direction.

It is additionally specifically noted that the controller 104 may beconfigured to use the machine learning classifier to determine the oneor more control signals configured to cause the translation of one ormore portions of the one or more metrology sub-systems 102. For example,the machine learning classifier may be configured to associate one ormore positions (e.g., positions along an x-axis and/or a y-axis) with adesired the one or more target features. In this regard, the machinelearning classifier may be configured to determine a position (e.g., acoordinate position on an x-axis and/or a y-axis) automatically centerthe one or more target features within a field of view.

In Step 408, one or more target images of the one or more targetfeatures captured at the one or more target focal positions arereceived. For example, the controller 104 may be configured to receiveone or more target images 135 from the metrology sub-system 102.

All of the methods described herein may include storing results of oneor more steps of the method embodiments in memory. The results mayinclude any of the results described herein and may be stored in anymanner known in the art. The memory may include any memory describedherein or any other suitable storage medium known in the art. After theresults have been stored, the results can be accessed in the memory andused by any of the method or system embodiments described herein,formatted for display to a user, used by another software module,method, or system, and the like. Furthermore, the results may be stored“permanently,” “semi-permanently,” temporarily,” or for some period oftime. For example, the memory may be random access memory (RAM), and theresults may not necessarily persist indefinitely in the memory.

It is further contemplated that each of the embodiments of the methoddescribed above may include any other step(s) of any other method(s)described herein. In addition, each of the embodiments of the methoddescribed above may be performed by any of the systems described herein.

One skilled in the art will recognize that the herein describedcomponents operations, devices, objects, and the discussion accompanyingthem are used as examples for the sake of conceptual clarity and thatvarious configuration modifications are contemplated. Consequently, asused herein, the specific exemplars set forth and the accompanyingdiscussion are intended to be representative of their more generalclasses. In general, use of any specific exemplar is intended to berepresentative of its class, and the non-inclusion of specificcomponents, operations, devices, and objects should not be taken aslimiting.

As used herein, directional terms such as “top,” “bottom,” “over,”“under,” “upper,” “upward,” “lower,” “down,” and “downward” are intendedto provide relative positions for purposes of description, and are notintended to designate an absolute frame of reference. Variousmodifications to the described embodiments will be apparent to thosewith skill in the art, and the general principles defined herein may beapplied to other embodiments

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations are not expressly set forth herein for sakeof clarity.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, other components. It isto be understood that such depicted architectures are merely exemplary,and that in fact many other architectures can be implemented whichachieve the same functionality. In a conceptual sense, any arrangementof components to achieve the same functionality is effectively“associated” such that the desired functionality is achieved. Hence, anytwo components herein combined to achieve a particular functionality canbe seen as “associated with” each other such that the desiredfunctionality is achieved, irrespective of architectures or intermedialcomponents. Likewise, any two components so associated can also beviewed as being “connected,” or “coupled,” to each other to achieve thedesired functionality, and any two components capable of being soassociated can also be viewed as being “couplable,” to each other toachieve the desired functionality. Specific examples of couplableinclude but are not limited to physically mateable and/or physicallyinteracting components and/or wirelessly interactable and/or wirelesslyinteracting components and/or logically interacting and/or logicallyinteractable components.

Furthermore, it is to be understood that the invention is defined by theappended claims. It will be understood by those within the art that, ingeneral, terms used herein, and especially in the appended claims (e.g.,bodies of the appended claims) are generally intended as “open” terms(e.g., the term “including” should be interpreted as “including but notlimited to,” the term “having” should be interpreted as “having atleast,” the term “includes” should be interpreted as “includes but isnot limited to,” and the like). It will be further understood by thosewithin the art that if a specific number of an introduced claimrecitation is intended, such an intent will be explicitly recited in theclaim, and in the absence of such recitation no such intent is present.For example, as an aid to understanding, the following appended claimsmay contain usage of the introductory phrases “at least one” and “one ormore” to introduce claim recitations. However, the use of such phrasesshould not be construed to imply that the introduction of a claimrecitation by the indefinite articles “a” or “an” limits any particularclaim containing such introduced claim recitation to inventionscontaining only one such recitation, even when the same claim includesthe introductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an” (e.g., “a” and/or “an” should typically beinterpreted to mean “at least one” or “one or more”); the same holdstrue for the use of definite articles used to introduce claimrecitations. In addition, even if a specific number of an introducedclaim recitation is explicitly recited, those skilled in the art willrecognize that such recitation should typically be interpreted to meanat least the recited number (e.g., the bare recitation of “tworecitations,” without other modifiers, typically means at least tworecitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,and the like” is used, in general such a construction is intended in thesense one having skill in the art would understand the convention (e.g.,“a system having at least one of A, B, and C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, and the like). In those instances where a convention analogousto “at least one of A, B, or C, and the like” is used, in general such aconstruction is intended in the sense one having skill in the art wouldunderstand the convention (e.g., “a system having at least one of A, B,or C” would include but not be limited to systems that have A alone, Balone, C alone, A and B together, A and C together, B and C together,and/or A, B, and C together, and the like). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

It is believed that the present disclosure and many of its attendantadvantages will be understood by the foregoing description, and it willbe apparent that various changes may be made in the form, constructionand arrangement of the components without departing from the disclosedsubject matter or without sacrificing all of its material advantages.The form described is merely explanatory, and it is the intention of thefollowing claims to encompass and include such changes. Furthermore, itis to be understood that the invention is defined by the appendedclaims.

What is claimed:
 1. A system, comprising: a controller communicativelycoupled to one or more through-focus imaging metrology sub-systems,wherein the controller includes one or more processors configured toexecute a set of program instructions stored in memory, and wherein theset of program instructions is configured to cause the one or moreprocessors to: receive a plurality of training images captured at one ormore focal positions, the plurality of training images including one ormore training features of a training specimen; generate a machinelearning classifier based on the plurality of training images capturedat the one or more focal positions; receive one or more target featureselections for one or more target overlay measurements corresponding toone or more target features of a target specimen; determine one or moretarget focal positions based on the one or more target featureselections using the machine learning classifier; receive one or moretarget images captured at the one or more target focal positions, theone or more target images including the one or more target features ofthe target specimen; and determine one or more overlay measurementsbased on the one or more target images.
 2. The system of claim 1,wherein the one or more through-focus imaging metrology sub-systemscomprise at least one of: an optical-based metrology sub-system or ascatterometry-based metrology sub-system.
 3. The system of claim 1,wherein the one or more focal positions comprise a plurality of focuspositions within a focus training range.
 4. The system of claim 1,wherein the plurality of training images is captured at the one or morefocal positions by translating one or more portions of the metrologysub-systems along one or more adjustment axes.
 5. The system of claim 4,wherein the set of program instructions is further configured to causethe one or more processors to: provide one or more control signals tothe one or more portions of the one or more metrology sub-systems,wherein the one or more control signals are configured to cause the oneor more portions of the one or more metrology sub-systems to betranslated along the one or more adjustment axes.
 6. The system of claim5, wherein the one or more control signals are configured to cause theone or more portions of the one or more through-focus imaging metrologysub-system to be centered.
 7. The system of claim 1, wherein determiningthe one or more overlay measurements based on the one or more targetimages of the one or more target features comprises: determining anoverlay between a first layer of the target specimen and a second layerof the target specimen based on a first overlay measurementcorresponding to one or more target features formed on the first layerof the target specimen and a second overlay measurement corresponding toone or more target features formed on the second layer of the targetspecimen.
 8. The system of claim 1, wherein the one or more targetfeature selections for the one or more overlay measurements is providedby a user via a user interface.
 9. The system of claim 1, wherein theset of program instructions is further configured to cause the one ormore processors to: provide one or more control signals to one or moreprocess tools.
 10. The system of claim 1, wherein the machine learningclassifier comprises at least one of a deep learning classifier, aconvolutional neural network, an ensemble learning classifier, a randomforest classifier, or an artificial neural network.
 11. A system,comprising: one or more through-focus imaging metrology sub-systems; anda controller communicatively coupled to the one or more through-focusimaging metrology sub-systems, wherein the controller includes one ormore processors configured to execute a set of program instructionsstored in memory, and wherein the set of program instructions isconfigured to cause the one or more processors to: receive a pluralityof training images captured at one or more focal positions, theplurality of training images including one or more training features ofa training specimen; generate a machine learning classifier based on theplurality of training images captured at the one or more focalpositions; receive one or more target feature selections for one or moretarget overlay measurements corresponding to one or more target featuresof a target specimen; determine one or more target focal positions basedon the one or more target feature selections using the machine learningclassifier; receive one or more target images captured at the one ormore target focal positions, the one or more target images including theone or more target features of the target specimen; and determine one ormore overlay measurements based on the one or more target images.
 12. Amethod of overlay measurement using one or more through-focus imagingmetrology sub-systems, comprising: receiving a plurality of trainingimages captured at one or more focal positions, the plurality oftraining images including one or more training features of a trainingspecimen; generating a machine learning classifier based on theplurality of training images captured at the one or more focalpositions; receiving one or more target feature selections for one ormore target overlay measurements corresponding to one or more targetfeatures of a target specimen; determining one or more target focalpositions based on the one or more target feature selections using themachine learning classifier; receiving one or more target imagescaptured at the one or more target focal positions, the one or moretarget images including the one or more target features of the targetspecimen; and determining one or more overlay measurements based on theone or more target images.
 13. The method of claim 12, wherein theplurality of training images and the one or more target images arecaptured by the one or more through-focus imaging metrology sub-systemscomprising at least one of: an optical-based metrology sub-system or ascatterometry-based metrology sub-system.
 14. The method of claim 12,wherein the one or more focal positions comprise a plurality of focuspositions within a focus training range.
 15. The method of claim 13,wherein the plurality of training images is captured at the one or morefocal positions by translating one or more portions of the metrologysub-systems along one or more adjustment axes.
 16. The method of claim15, further comprising: providing one or more control signals to the oneor more portions of the one or more through-focus imaging metrologysub-systems, wherein the one or more control signals are configured tocause the one or more portions of the one or more through-focus imagingmetrology sub-systems to be translated along the one or more adjustmentaxes.
 17. The method of claim 16, wherein the one or more controlsignals are configured to cause the one or more portions of the one ormore through-focus imaging metrology sub-systems to be centered.
 18. Themethod of claim 12, wherein determining the one or more overlaymeasurements based on the one or more target images of the one or moretarget features comprises: determining an overlay between a first layerof the target specimen and a second layer of the target specimen basedon a first overlay measurement corresponding to one or more targetfeatures formed on the first layer of the target specimen and a secondoverlay measurement corresponding to one or more target features formedon the second layer of the target specimen.
 19. The method of claim 12,wherein the one or more target feature selections for the one or moreoverlay measurements is provided by a user via a user interface.
 20. Themethod of claim 12, further comprising: providing one or more controlsignals to one or more process tools.
 21. The method of claim 12,wherein the machine learning classifier comprises at least one of a deeplearning classifier, a convolutional neural network, an ensemblelearning classifier, a random forest classifier, or an artificial neuralnetwork.